MLflow for LLM Ops: Track, Evaluate, and Govern Your AI Models

MLflow started as an experiment tracker for traditional ML. Today it is the backbone of LLM operations — tracking prompts, evaluating outputs, managing model versions, and governing AI deployments.

What MLflow Does for LLMs

• LLM Tracking — Log every prompt, response, token count, and cost. Compare runs side by side.
• Evaluation — Built-in metrics for toxicity, relevance, faithfulness, and hallucination detection. Evaluate RAG pipelines end-to-end.
• Tracing — Distributed tracing for the full chain: prompt to retrieval to generation. See exactly where things go wrong.
• AI Gateway — Unified API gateway for multiple LLM providers with rate limiting and credential management.

The Model Registry

Every model in production should be registered, versioned, and tracked. MLflow's model registry provides aliases, lineage tracking, and approval workflows — essential for regulated industries where you need to prove which model made which decision.

Getting Started

Start by wrapping your existing LLM calls with MLflow tracking. Log inputs, outputs, and metadata. Within a week, you will have visibility into how your AI is performing that you never had before. That visibility is the foundation for everything else — evaluation, optimization, and governance.